Search and retrieval of electronic documents using key-value based partition-by-query indices

ABSTRACT

Methods and systems for providing a search engine capability for large datasets are disclosed. These methods and systems employ a Partition-by-Query index containing key-values pairs corresponding to keys reflecting concept-ordered search phrases and values reflecting ordered lists of document references that are responsive to the concept-ordered search phrase in a corresponding key. A large Partition-by-Query index may be partitioned across multiple servers depending on the size of the index, or the size of the index may be reduced by compressing query-references pairs into clusters. The methods and systems described herein may to provide suggestions and spelling corrections to the user, thereby improving the user&#39;s search engine experience while meeting user expectations for search quality and responsiveness.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/468,979, filed May 10, 2012, which claims priority to U.S.Provisional Application 61/484,298, filed May 10, 2011, which isincorporated herein in its entirety.

TECHNICAL FIELD

The present disclosure is directed to the field of information systemsand, more particularly, to methods and systems for performinginformation retrieval involving large amounts of documents.

BACKGROUND

The Internet has become a popular foundation for modern commerce andpersonal communication. This popularity can be attributed to manyfactors, including the ease with which people can use the Internet andthe amount of information available on the Internet. As more informationbecomes available on the Internet, it will become even more difficult tolocate and retrieve useful information unless search methods keep pacewith the volume of information.

Search engines must balance accuracy with speed. Users expect thatrelevant search results will be delivered in seconds, although theamount of electronic data that is being searched is growingexponentially. Users also expect search engines to find the informationdesired by the user even if the user gives incorrect or incompleteinformation. Many existing search engines correct spelling mistakes,find approximate matches, or provide suggestions to the user, basedeither on the user's prior use or overall popularity of the information.

Existing search engines will face difficulties keeping pace with thegrowth in available searchable data because of the way they searchinformation. Existing search engines typically operate by creating anindex of available documents or information prior to receiving anysearch queries and by searching that index for user-provided terms in asearch query upon receipt of that query. While this may work well with asmall amount of data, it becomes impractical as the volume of datagrows.

Traditional search engines often create an index using a two-stepprocess. First, a “forward index” is created for each document in thecorpus. A “forward index” consists of a unique ordered list of wordswithin a document created by parsing each word in that document,removing redundant words, and associating those words with theircorresponding documents. For example, the forward index for a firstdocument (D1) containing the sentence “Sam I am” is “am, I, sam” whichthe forward index for a second document (D2) containing the sentence “Ido not like green eggs and ham” is “and, do, eggs, green, ham, I, like,not.” As shown in these examples, one document may be associated withmany individual words.

Second, an “inverted index” for a corpus is formed by first reversingeach association between a document and its list of words and thencombining the documents associated with each word into a single list. Alist of documents associated with a search term is referred to as a“posting list.” For example, for a corpus containing documents D1 and D2discussed above, the inverted index for the corpus would be: “and:D2”,“do:D2”, “eggs:D2”, “green:D2”, “ham:D2”, “|:D1 & D2”, “like:D2”,“not:D2”, and “sam:D1”. Note that the word “I” is associated withdocuments D1 and D2 while all other words are associated with eitherdocument D1 or D2.

Traditional search engines identify documents responsive to a searchquery based on an union of the posting lists and prioritization of theresults. For example, for a corpus containing D1 and D2, a search queryfor documents containing the word “sam” would return only document D1because the inverted index only associates the word “sam” with documentD1. Alternatively, a search for documents containing the phrase “do youlike Sam” may return a prioritized search result of documents D2 and D1,reflecting that document D2 contains the words “do” and “like” andtherefore may be more relevant, whereas document D1 only contained theword “sam”.

An inverted index for a relatively small amount of data can bemaintained in memory rather than being stored on disk or in a database,thereby allowing acceptable search performance. When a corpus is large,however, the data is partitioned across multiple machines in anorder-preserving manner, a process known as “sharding”. Conventionalsearch engines split the indices for a corpus by document, rather thansplitting the indices by some other characteristic. Such split indicesare referred to as “partition-by-document” indices. When partitioning inthis manner, search queries must be broadcast to each machine, and theresults from each machine are prioritized and combined, a time-consumingand slow process.

Traditional search engines suffer from performance limitations not justfrom sharding, but also from the way information is retrieved.Traditional relational databases were designed to retrieve datastructured in a consistent format and are not effective for storing orretrieving unstructured data, such as an inverted index. NoSQL is akey-value storage system of storing or retrieving data from very largedata sets. NoSQL systems can store significant amounts of data and canperform key-value searches very quickly relative to other searchsystems, but cannot support inverted indexes efficiently usingtraditional search methods such as partition-by-document indexing.

SUMMARY

Methods and systems for performing the following steps are disclosed:generating, by a computing device, a query index based on the set ofelectronic documents, wherein the query index comprises a first set ofkey-value pairs, each key-value pair comprising a key and one or morereference values, each key comprising at least one token from anexpected query generated based on the set of electronic documents, andeach reference value corresponding to a document associated with the atleast one token; parsing, by the computing device, a query by a computeruser into at least one token; generating, by the computing device, anordered query from the parsed query comprising tokens ordered byincreasing frequency within the set of electronic documents; andproviding, by the computing device, document references responsive tothe query by the computer user based on the ordered query and the queryindex.

Methods and systems are also disclosed for creating a query indexconfigured to store document references responsive to a plurality ofexpected queries, the method comprising: generating, by a computingdevice, a first plurality of expected queries from an electronicdocument in a set of electronic documents based at least in part ontokens parsed from the electronic document; determining, by thecomputing device, relevance of each expected query to the electronicdocument; selecting, by the computing device, a second plurality ofexpected queries for each electronic document from the first pluralityof expected queries for that electronic document based at least in parton the relevance of each expected query in the second plurality ofexpected queries to that electronic document; and performing, by thecomputing device, the following steps for each expected query in thesecond plurality of expected queries: ordering tokens in the expectedquery by decreasing relevance to form an ordered expected query; andcreating a plurality of document references, wherein the plurality ofdocument references includes a reference to the electronic document andat least one reference to another document containing the tokens of theexpected query in the set of electronic documents; ordering theplurality of document references by decreasing relevance to the expectedquery to form an ordered list of document references; and creating afirst key-value pair for the ordered expected query, wherein the key forthe first key-value pair comprises the ordered expected query and thevalue for the first key-value pair comprises the ordered list ofdocument references.

Methods and systems for configuring a search engine to provide spellingcorrections or suggestions to search queries are also disclosed,comprising: generating, by a computing device, residual strings withassociated weights for each token in a corpus; generating, by acomputing device, direct producer lists for each token and residualstring; forming, by a computing device, indirect producer lists for eachtoken by propagating direct producer lists; and propagating, by acomputing device, tokens with corresponding weights for each token.

Methods and systems for generating a list of the most relevantsuggestions or spelling corrections to a search engine user from acollection of suggestions or spelling corrections are also disclosed,comprising: generating, by a computing device, confusion sets for eachtoken in a search query; generating, by the computing device, aconfusion matrix from the confusion sets; ranking, by the computingdevice, suggestions in the confusion matrix by the vector-space anglebetween the search query and the suggestions; and selecting, by thecomputing device, each ranked suggestion whose vector-space anglebetween the search query and the ranked suggestion is less than avector-space angle between the search query and a document associatedwith a higher-ranked suggestion.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate a method for creating a Partition-by-Queryindex from a corpus according to at least one embodiment.

FIG. 2 illustrates several clustered series of key-values pairs in anindex consistent with at least one embodiment.

FIG. 3 illustrates a system employing a Partition-by-Query index splitacross three servers consistent with at least one embodiment.

FIG. 4 illustrates a method for responding to a search query requestusing a Partition-by-Query index with portions stored at a plurality ofservers.

FIG. 5 illustrates a method for retrieving search results from aPartition-by-Query index consistent with at least one embodiment.

FIG. 6 illustrates a method for generating a confusion set consistentwith at least one embodiment.

FIG. 7A illustrates a series of residual strings with correspondingweights for a token in at least one embodiment.

FIG. 7B illustrates relationships between two residual strings and atoken in at least one embodiment.

FIG. 8 illustrates a method for providing suggestions and spellingcorrections to users based in part on a confusion set in at least oneembodiment.

FIG. 9 illustrates a method applying the principles of the TriangleInequality to identify one or more suggestions for a search query.

DETAILED DESCRIPTION

Embodiments described herein utilize a novel “partition-by-query”approach for generating search results by analyzing documents in acorpus to identify queries to which a document would be responsive(“expected queries”), aggregating and prioritizing by relevancedocuments that are responsive to each expected query to form aprioritized list of documents responsive to each expected query, anddelivering a prioritized list of documents responsive to a particularsearch query upon receiving that search query from a user.

The methods and systems described herein may provide a search resultfaster and less expensively than existing methods and systems,especially for corpuses that are very large, while still meeting users'search engine expectations for responsiveness, search engine resultquality, and ease of use.

Embodiments may also provide capabilities for correcting spellingmistakes that users make while inputting search queries to a searchengine. This spelling correction capability may improve search enginespeed and responsiveness, thereby meeting or exceeding users'expectations.

Embodiments may additionally provide capabilities for offering real-timesearch query suggestions. With the suggestion capability, search termsor characters that are known to exist in the corpus may be identifiedand suggested to the user as the user enters characters into the searchtext box.

Also described herein are methods and systems for reducing the size ofthe search engine index. Reducing the size of the search engine indexmay provide faster responses to search engine queries and lower costattributable to maintaining the search engine index.

Embodiments may also incorporate a MapReduce capability. The MapReducecapability analyzes documents in a corpus and generates key-value pairsthat will be utilized when compiling a Partition-by-Query index. Use ofa MapReduce capability during document analysis may reduce documentanalysis cost and development time to generate an index for a searchengine employing the Partition-by-Query approach.

Embodiments may employ some, all, or none of the aforementionedextensions to the Partition-by-Query approach. Each of these extensionsis independent from other extensions and so each extension may beselectively employed to meet the challenges of different search engineenvironments. The discussion that follows begins with an explanation ofthe Partition-by-Query approach to providing search results.

Partition by Query

In general, the Partition-by-Query approach to providing search resultsdescribed herein may be described as having two stages. In a firststage, an initial Partition-by-Query index is generated for use inproviding responses to search queries. The Partition-by-Query index maybe generated anew or may involve supplementing an existingPartition-by-Query index to account for additional documents added tothe corpus since the existing Partition-by-Query index was generated. Ina second stage, at least part of a search query is received from a user,and the Partition-by-Query index is used to provide one or more searchresults to the user.

FIGS. 1A and 1B illustrate a method for creating a newPartition-by-Query index from a corpus according to at least oneembodiment of the present invention. As shown in FIG. 1A, creation ofthe index may begin with a first document being selected from the corpusfor processing (step 102).

Parsing a Selected Document into Tokens

In step 104, the selected document is parsed into tokens. Parsinginvolves a process of identifying words or symbols within a document. Insome embodiments, punctuation and text formatting are ignored whenparsing. “Spatial distance” refers to the number of intervening wordsbetween two tokens in a document or whether two tokens are presentwithin a single sentence or a single paragraph within a document. Aparsing approach that ignores spatial distance between tokens issometimes referred to as “bag of words” parsing. In some embodiments,the spatial distance between tokens may be determined and theinformation provided to step 108 so that the spatial distance betweentokens may be considered when making relevance determinations.

In some embodiments, a “stop list” may also be utilized when parsing adocument. A stop list may direct the parser to ignore certain tokensthat would not be useful to forming relevant search queries from theselected document. Grammatical articles such as “a”, “an”, and “the” areexamples of tokens frequently in stop lists. For example, in most cases,the article “the” does not meaningfully distinguish search queriesemploying that token (e.g., “the Boston Red Sox”) from search queriesomitting that token (e.g., “Boston Red Sox”). Both search queries couldbe expected to provide the same prioritized list of responsive documentsif the prioritized list of responsive documents was generated robustly.

Yet, some search queries may also be meaningfully distinguished bycommon articles such as “the”. For example, some formal names or titlesinclude articles or other tokens that would otherwise be primecandidates for inclusion in a stop list. Specific examples may includemusical groups (e.g., “The Doors” or “The The”) and literary works (ThePrince by Machiavelli) whose names may be difficult to distinguish fromother subjects without including articles or other tokens in searchqueries. Therefore, some embodiments may not utilize a stop list or mayremove tokens in a stop list in context-specific situations.

Notwithstanding the context-specific situations discussed above,utilizing a stop list during parsing of documents in the corpus mayimprove the quality of the resulting Partition-by-Query index byavoiding substantively redundant keys (i.e., search queries which onlydiffer from another search query by the presence or absence of a tokenon the stop list). Therefore, use of a stop list during parsing mayprovide benefits in some embodiments.

Generating Search Queries from Tokens

In step 106, one or more search queries to which the selected documentwould be responsive are generated from the tokens. In certainembodiments, search queries are generated using the Generative Model. Incertain embodiments consistent with the principles described herein, theGenerative Model is a function that utilizes a hypergeometricdistribution combined with the principles of Monte Carlo simulation todetermine queries to which the selected document would be responsive. Anoverview of hypergeometric distributions and Monte Carlo simulationfollows.

Hypergeometric distributions reflect a probability that a particularcombination of conditions will occur for a given collection ofconditions such that the distribution accounts for how the existence ofone condition affects the likelihood that another condition will exist.A classic exemplary application of a hypergeometric distribution isdetermining a probability that four marbles randomly selected from a jarwill all be a first color when the jar contains 10 marbles of the firstcolor and 10 marbles of a second color. As each successive marble isremoved from the jar, the odds of the next marble being of the firstcolor are reduced relative to the likelihood of a marble of the secondcolor being chosen next. Thus, as this example illustrates,hypergeometric distributions generate probabilities that reflectchanging conditions.

Generating search queries from a list of tokens in a document andassigning probabilities to those search queries lends itself toemploying a hypergeometric distribution. For example, a document maycontain 1000 tokens, including 100 unique tokens. A first unique tokenmay be included twelve times among the 1000 tokens. Therefore, aprobability of a search query being formed from a first token and asecond token may consider the probability of the first token beingselected from the 1000 tokens and the second token being selected from988 remaining tokens. A hypergeometric distribution accounts for therelationship between token selection and the number of remaining tokensfrom which to select.

Hypergeometric distributions have been shown to generate accurateprobabilities for search queries containing terms within a document.See, for example, Hypergeometric Language Model and Zipf-Like ScoringFunction for Web Document Similarity Retrieval, Felipe Bravo-Marquez etal., String Processing and Information Retrieval, Volume 6393/2010 at303, 305. Hypergeometric distributions can be used to determine thelikelihood of a particular search query being issued against aparticular document based on the distribution of tokens within thatdocument. For example, a series of prioritized search queries can bedetermined for a document, where the priority of a particular searchquery is determined by ranking its probability of being issued againstthe document relative to the probability of other search queries beingissued against the document.

Monte Carlo simulation involves making a series of random selectionsfrom a collection of available selections, and utilizing those randomselections as inputs to a function to generate a series of results. InMonte Carlo simulation, the accuracy of the collective series ofsimulation results is somewhat determined by the number of randomselections employed to generate the results. As the number of randomselections input to a function approaches the number of availableselections in the collection of available selections, the accuracy ofMonte Carlo simulation results asymptotically approaches the result thatwould have been achieved through applying formal rather than iterativeanalytical methods.

For example, calculus is a formal method for determining an area under acurve. By contrast, a Monte Carlo simulation can approximate an areaunder a curve by randomly selecting points within a region containingareas under the curve and above the curve; i.e., each point is eitherunder the curve or above the curve. As the number of random points inthe simulation increases, the ratio between the number of points underthe curve and the number of total points in the region asymptoticallyapproaches the ratio between the actual area under the curve and thetotal area of the region. Monte Carlo simulation can provide aniterative but asymptotically accurate solution to problems reflectingprobabilities and distributions. Monte Carlo simulation can beespecially useful for problems that are difficult or intractable tosolve by formal methods.

In methods and systems described herein, Monte Carlo simulation can beutilized to randomly generate multi-word search queries from the tokensparsed from a document. In embodiments described herein, thePartition-by-Query index may have keys (i.e., search queries) ranging inlength from one token to multiple tokens. If the maximum number oftokens (M) per key is, for example, five, Monte Carlo simulation may beused to randomly generate N search queries, each comprised of one tofive tokens. While M may be any number, longer search phrases lead tolarger indexes, which many conventional search methods cannot handle.The systems and methods described herein allow for larger indices thanexisting search methods.

In step 106, the process of randomly selecting tokens and computingtheir likelihood through Monte Carlo simulation is repeated for N numberof tokens. If a sufficiently large number of Monte Carlo simulations arerun (i.e., if N is a sufficiently large number), an accurate reflectionof the various queries that a document would be responsive to can begenerated because the Monte Carlo simulation result shouldasymptotically approach a theoretically accurate result.

For example, in some embodiments, the number of Monte Carlo simulationsrun for each length of search query may range linearly from a startingvalue for one-term search queries to an ending value for themaximum-length search queries. In another example, the number of MonteCarlo simulations run for each length of search query may varylogarithmically from a starting value for one-term search queries or mayvary as reflected in a function. Those skilled in the art will recognizeother ways to distribute the number of Monte Carlo simulations to be runin total (N) between the various search query lengths employed in thePartition-by-Query index without departing from the spirit of theembodiments discussed herein.

Using a Language Model to Select Relevant Search Queries

In step 108, a subset of the most relevant search queries for theselected document is identified from among the search queries generatedin step 106. The most relevant subset may be selected by, for example,applying a Language Model. Those skilled in the art will recognize thatmany language models are available, and that particular language modelssuit a particular purpose better or worse than other language modelsdepending on circumstances relating to the particular purpose.

In at least some embodiments described herein, the Term Frequency,Inverse Document Frequency (“TF-IDF”) language model may be utilized toevaluate search queries. In certain embodiments, Okapi BM25 is utilizedto evaluate search queries. In still other embodiments, models orfunctions other than TF-IDF and Okapi BM25 may be used.

A language model in the context of the present application determineshow relevant a search query is for a particular document based on afrequency with which each term is present in the document and thefrequency with which that term is present in other documents within thecorpus. Therefore, a language model distinguishes search queriescontaining tokens that are present in a document and are common indocuments within a corpus from search queries containing tokens that arepresent in a document but are rare in documents within the corpus. Asearch query that is common in a particular document but is rare in thecorpus as a whole reflects a particularly relevant search query forlocating that document.

Once the relevance of each search query has been determined by applyinga language model as discussed above, the M most relevant search queriesfor a document can be identified in step 108.

Concept Ordering of Search Query Tokens

In step 110, each search query identified in step 108 as “most relevant”is ordered by concept prior to building a key-value index of searchqueries. One measure of proper concept ordering is that when performedproperly it will generate keys with common roots for search queriesreflecting similar concepts; i.e., if two search queries are directed tosimilar concepts, keys for those for those search queries should containsimilar initial tokens. By generating keys with common roots for searchqueries reflecting similar concepts, concept ordering allows queriesreflecting similar concepts to be grouped together in a manner thatallows for storage efficiency and for efficient implementation ofspelling correction and search suggestions, as discussed below.

Concept ordering utilizes token frequency as a measure of how importanta token is to a search query. Tokens that are relatively common indocuments within the corpus (e.g., “2012”) have a high frequency whiletokens that are relatively rare in documents within the corpus have alow frequency. Therefore, tokens with low frequency (e.g., “Obama”)within a search query are more likely to describe the specific conceptsfor which documents are sought. Accordingly, tokens with low frequencyare more important to the search query than tokens with high frequency.In some embodiments, token frequency may be determined by languagemodels such as those discussed in conjunction with step 108.

Concept ordering reorders the list of tokens within each search query toreflect an ordering based on frequency, with lower frequency tokenspreceding higher frequency tokens. After concept ordering has beenperformed for each search query, each search query begins with a tokenthat is most relevant to the concepts expressed by the search query andproceeds with tokens of decreasing relevance. This token ordering allowssearch queries directed to related concepts to be lexicographicallysimilar to each other.

For example, search queries “President Obama reelection” and “PresidentObama 2012” may seek documents reflecting similar concepts—PresidentObama's 2012 reelection campaign. The search query “President Obama” isalso somewhat similar to the two search queries noted above, althoughsearch query “President Obama” is broader in concept than the searchqueries including tokens “reelection” or “2012.”

Under one language model, for the search query “President Obama 2012,”“Obama” may be the least frequent token within that search phrase for aparticular document. Therefore, concept ordering places the token“Obama” as the first token in a concept-ordered index key formed fromthe search query “President Obama 2012.” For the same language model,“President” may be the second-least frequent token and “2012” may be themost-frequent token. Therefore, the concept-ordered token list for thesearch query “President Obama 2012” would be the tokens “Obama,”“President,” and “2012.”

In some embodiments, tokens in a concept-ordered list are preceded by acorresponding frequency metric. Additionally, in some embodiments, everytoken in a concept-ordered list is preceded by a frequency metriccorresponding to that token. In other embodiments, only some tokens arepreceded by corresponding frequency metric; e.g., a second and eachsubsequent token in a concept-ordered key is preceded by a frequencymetric corresponding to that token. In some embodiments, a frequencymetric may be a hexadecimal number (e.g., 00CF01) while in otherembodiments the frequency metric may be represented in another format.

For example, for a search query “President Obama 2012,” the token“President” may have a frequency metric of “00C329,” token “Obama” mayhave a frequency metric of “00001A,” and token “2012” may have afrequency metric of “05623B.” Therefore, in this example, an orderedtoken list for the search query “President Obama 2012” may include thefollowing: “00001A,” “Obama,” “00C329,” “President,” “05623B,” and“2012.”

In some embodiments, token and frequency metric padding and/or token andfrequency metric delimiters may be utilized to distinguish betweentokens and corresponding frequency metrics within a key. For example,token and frequency metric padding can establish a beginning and endingof each token or frequency metric within a key by specifying a fixedlength for each token or frequency metric within a key and extending atoken or frequency metric to that fixed length with a specific padcharacter. In some embodiments, the padded length of a token may matchthe padded length of a frequency metric while in other embodiments theselengths may differ.

In some embodiments, the pad characters precede the token or frequencymetric and in other embodiments the pad characters follow the token orfrequency metric. In some embodiments, low numbers as frequency metricsmay represent low token frequencies while high numbers as frequencymetrics may represent high token frequencies. In other embodiments, lownumbers as frequency metrics may represent high token frequencies andhigh numbers as frequency metrics may represent low token frequencies.For example, for a token and frequency metric length of 10 charactersand a pad character “0,” the token “Obama” may be padded to form“Obama00000” and the frequency metric “00C329” may be padded to form“000000C329.”

In another embodiment, delimiters may be used to distinguish betweenfrequency metrics and tokens within a key. In at least one embodiment,the “|” character may delimit frequency metrics and tokens within a key.For example, “00001A|Obama|OOC329|President|05623B|2012” may represent akey employing delimiters for the tokens and frequency metrics discussedabove. In other embodiments, delimiter characters other than “|” may beused, including delimiter characters that are machine recognizable butnot human recognizable. In additional embodiments, more than onedelimiter character may be employed to distinguish between tokens andfrequency metrics within a key. In some embodiments, padding anddelimiters may both be employed to distinguish between tokens andfrequency metrics within a key.

In step 112, a key-value pair comprising a concept-ordered search queryand a reference to the selected document is created. At the conclusionof step 112, a series of key-value pairs exist for the most relevantsearch queries.

In step 114, a determination is made whether all documents in the corpushave been processed. If all documents have not been processed, a nextdocument is selected (step 116), and the method continues with step 104.Steps 104-112 are repeated for each document in the corpus to generate aseries of search queries and corresponding document references for alldocuments in the corpus.

Once all documents in the corpus have been processed (step 114), themethod continues as shown in FIG. 1B.

Combining Values for Keys that are the Same or Substantially Similar

In step 118, key-value pairs (search query and document reference pairs)that have the same or substantially similar keys are combined to form asingle key with a list of corresponding values (referred to hereinafteras a query-references pair). Each reference in the list of referencesrefers to a document that is responsive to the search query.

For example, if documents D1 and D2 are both relevant documents for thesearch query “President Obama 2012,” step 118 may generatequery-references pair (0.32|Obama|0.15|President|0.03|2012; D1, D2) bycombining references in key-value pairs identifying documents D1 and D2into a single query-references pair reflecting both documents in thelist of references for that query. In another example, document D3 is arelevant document for the search query “President Obama 2012 again” andthe token “again” has a relevance metric of “0.01.” Therefore, searchquery “President Obama 2012 again” has a concept-ordered key of“0.32|Obama|0.15|President|0.03|2012|0.01|again.”

Concept ordering of search queries allows identification of searchqueries that are substantially similar and consolidation of such searchqueries into a single search query. In the examples above, alexicographical sort of concept-ordered keys for documents D1, D2, andD3 allows for an identification that search queries “President Obama2012” and “President Obama 2012 again” are only distinguished by asingle token whose relevance metric is low by comparison to other tokensbecause the first three concept-ordered tokens for each search query arethe same. Therefore, these search queries and their associated documentscan be consolidated into a single search query. Thus, in this example, asingle concept-ordered query (“President Obama 2012”) and references todocuments D1, D2, and D3 may form a query-references pair as discussedabove.

The quality of a consolidated search query result may not bemeaningfully reduced relative to the relevance of two separate searchqueries because removed tokens have relatively low relevance metrics byconstruction. In other embodiments, different or additional evaluationsmay be performed to determine whether key-value pairs can beconsolidated to form a query-references pair. Yet, benefits may berealized by consolidating search queries that are substantially similar.For example, a smaller Partition-by-Query index may be faster to use forproviding search engine results and may be faster to generate, therebyproviding efficiencies during operation. Additionally, smallerPartition-by-Query indexes may reduce operational costs such as diskstorage and hardware needs.

Creating Prioritized Lists of Responsive Documents

In step 120, a prioritized list of values is generated for each key. Foreach query-references pair of concept-ordered search query tokens andresponsive documents generated in step 118, the document referenceswithin that query-references pair are prioritized by the key's relevanceto associated documents. In some embodiments, a language model isemployed to prioritize the document references within values of thekey-values pair. In some embodiments, the language model employed toprioritize document references may be the same language model that wasemployed in step 108 while in other embodiments a different languagemodel may be employed. The language model that is employed, however,will determine how relevant a document as a whole is to a search queryrepresented in the key of a concept-ordered key-values pair.

In at least one embodiment, if document D1 contained one instance ofeach of tokens “President,” “Obama,” and “2012,” while document D2contained multiple instances of each token, document D2 would be morerelevant to the search query “President Obama 2012” than document D1.Therefore, in that embodiment, document D2 would be prioritized higherthan document D1 in the list of document references in a correspondingquery-references list for search query “President Obama 2012.”Embodiments may also or alternatively consider factors such as length ofthe document and proximity of search query tokens within the document.Upon completion of step 120, each query-references pair contains aseries of values, with each value comprising a document reference,ordered by relevance to the search query.

Combining Key-Values Pairs into Clusters

In step 122, the query-references pairs are compressed into a series ofquery-references pair clusters. One benefit from combiningquery-references pairs into clusters is that the size of thePartition-by-Query index may be reduced. Reducing the size of the indexwill reduce the amount of storage consumed by the Partition-by-Queryindex.

In some embodiments, the Partition-by-Query index is maintained in aNoSQL or similar data store. Those skilled in the art will recognizethat NoSQL stores key-value data efficiently while providing redundancyand relatively high performance. Similar implementations includeGoogle's BigData and Apache's Hadoop.

Data stores similar to NoSQL, BigIndex, and Hadoop typically provide twofunctions for retrieving data: a “get( )” function that retrieves avalue for a specific key in the data store if that specific key ispresent in the data store; and a “scan( )” function that retrieves thefirst key lexicographically following a specific key in the data store,whether or not that specific key is actually present in the data store.Embodiments utilizing a scan( ) function within a data store canefficiently store and retrieve clustered query-references pair data byusing a key corresponding to the last key in a clustered series ofquery-references pairs and storing the query-references pairs as thevalue within the clustered query-references pair.

Moreover, a Partition-by-Query index can efficiently use a scan( )function, such as those provided by NoSQL, BigData, or Hadoop, toretrieve data within a clustered key. The clustered key is keyed off thelast lexicographically ordered key in the cluster because the scan( )function returns the first entry in the index lexicographicallyfollowing a key provided as a parameter to the scan( ) function. Anexample may illustrate these points.

Adding Documents to a Partition-By-Query Index

Although method 100 provides a capability to create a Partition-by-Queryindex, as discussed above, method 100 could be modified to add documentsto a corpus by incorporating document references for the additionaldocuments into an existing Partition-by-Query index. In at least oneembodiment, steps 102, 104, 106, 108, 110, 112, 114, 116, 118, and 120may be executed on a collection of documents to be added to a corpus.Upon completion of step 120, new query-references pairs that do notpresently exist in the Partition-by-Query index but were generated fromthe documents to be added to the index may be added to thePartition-by-Query Index. Additionally, new references for existingqueries may be prioritized relative to existing references for theexisting queries and added to appropriate locations in the clusteredPartition-by-Query index.

Also, the query-references pairs clusters may be adjusted to rebalancethe distribution of queries stored in a portion of a Partition-by-Queryindex stored on a particular server. For example, if documents added tothe Partition-by-Query index involve queries that are associated with aportion of the index stored on a particular server, rather than beingdistributed somewhat evenly between index portions on all servers, oneindex portion may be disproportionately large relative to other indexportions. In this situation, some queries may be moved from one indexportion to another index portion to achieve a reasonable balance betweenindex portions.

FIG. 2 illustrates several clustered query-references pairs in an index200 consistent with at least one embodiment. As shown, index 200comprises a series of key-value pairs, labeled Query₁-References₁,Query₂-References₂, Query₃-References₃, Query₄-References₄,Query₅-References₅, Query₆-References6, Query₇-References₇,Query₈-References₈, and Query₉-References₉. In this embodiment, index200 contains nine query-references pairs. Additionally, thequery-references pairs are combined into clusters with threequery-references pairs per cluster. As shown, index 200 containsclusters 202, 204, and 206.

As discussed above, clusters use the lexicographical last key (query)within a cluster as the key for the cluster. Therefore, clusters 202,204, and 206 would use Query₃, Query₆, and Query₉ as keys. Additionally,clusters 202, 204, and 206 would use the three query-references pairsassociated with the key as values for each corresponding cluster. Forexample, values corresponding to Query₃ in cluster 202 may beQuery₁-References1, Query₂-References₂, and Query₃-References₃.Similarly, values corresponding to Query₆ in cluster 204 may beQuery₄-References₄, Query₅-References₅, and Query₆-Referencess.

Combining query-references pairs in this way may allow for efficientstorage and retrieval of data stored in these combined query-referencespairs. For example, utilizing the combined form of index 200 discussedabove, documents responsive to a concept-ordered search phrase Query₅may be retrieved by providing Query₅ as an input to a scan( ) function,which leads to the scan( ) function selecting the cluster whose key isQuery₆, and receiving the Query₄-References₄, Query₅-References₅, andQuery₆-References₆ data in response. From that data, References₅ can beretrieved from the Query₅-References₅ portion of the data.

Additionally, in some embodiments, efficiencies can be realized byperforming data compression on the combined key-values pairs prior tostoring that pair data in the Partition-by-Query index. For example, aZIP compression algorithm could be employed to perform losslesscompression of the data within a cluster. Those skilled in the art willrecognize that other compression algorithms could alternatively be usedwithout departing from the spirit of the discussion above.

Storing the Partition-By-Query Index in One or More Servers

In step 124, the Query-References pair clusters are stored as a singlePartition-by-Query index for use in responding to search queries. ThePartition-by-Query index may be stored on a single machine. In someembodiments, method 100 concludes with step 124.

In some embodiments, the index is split in step 126 into a plurality ofpartial Partition-by-Query indexes (“portions”) that are stored onseparate servers. One reason for splitting a Partition-by-Query indexinto a plurality of portions is to expand the capability of a searchengine to handle more requests than a single server could handle byspreading the queries across servers. Because each server can onlyrespond to a limited number of search queries within a period of time,consumer demand or other factors may require that more than one serverbe used.

FIG. 3 illustrates a system 300 employing a Partition-by-Query indexsplit across three servers consistent with at least one embodiment. Asillustrated in FIG. 3, system 300 comprises a proxy 302, a first server304, a second server 306, and a third server 308. First server 304contains a first portion 310 of a Partition-by-Query index. Secondserver 306 contains a second portion 312 of the Partition-by-Queryindex. Third server 306 contains a third portion 314 of thePartition-by-Query index. The portions may be generated, for example, bymethods described herein. Each server (304, 306, and 308) contains atleast one processor, memory, and a network interface card. In someembodiments, each server may also contain a high-speed disk. Proxy 302contains at least one processor, memory, and a network connection. Insome embodiments, proxy 302 is coupled to each of server 304, 306, and308 through a network switch (not shown). In other embodiments, proxy302 may be coupled to each of server 304, 306, and 308 through anetworking link other than a network switch.

FIG. 4 illustrates a method for responding to a search query requestusing a Partition-by-Query index with portions stored at a plurality ofservers, such as shown in FIG. 3. In step 402, a search engine requestin the form of a search query is received, for example, at proxy 302. Instep 404, proxy 302 determines which server, if any, contains responsivedata for that search query. If proxy 302 determines that one of theservers contains responsive data, proxy 302 forwards the search query tothat single server in step 406.

Notably, the search query can be forwarded to a single server sinceproxy 302 knows the identity of the server containing results for thesearch query based on a list of queries or a lexicographical range ofqueries for each server maintained by the proxy 302. This featurereduces network traffic within system 300 and improves the ability ofsystem 300 to respond to large numbers of user requests.

In step 408, the identified server receives the forwarded search queryfrom proxy 302 in a form consistent with a particular embodiment andretrieves an ordered list of responsive documents from that server'scorresponding portion of the Partition-by-Query index. In step 410, theidentified server sends the retrieved list to proxy 302.

In step 412, proxy 302 forwards the ordered list of responsive documentsto the requesting user as part of responding to the requesting user'ssearch engine request. In some embodiments, proxy 302 may also formatthe ordered list of responsive documents to make the list more pleasingto the requesting user. By this method, proxy 302 and the identifiedserver interact to store and retrieve search query results efficientlyand at a rate that potentially exceeds the capacity of a single serverto receive and process search query results.

Using a MapReduce Capability to Generate the Index

As mentioned above, efficiencies can be realized by partitioning acorpus into subsets, each of which is provided as the corpus to themethod as shown in FIGS. 1A and 1B. The principles described herein alsomay be combined with a MapReduce framework and libraries to realizegreater computational throughput. After each independent portion of theproblem has been executed, the results from each independent executionmay be combined to form a combined result reflecting the result thatwould have been generated by a single server executing the originalproblem.

In some embodiments of the present invention, a MapReduce capability canbe utilized in conjunction with the method shown in FIGS. 1A and 1B bysplitting a corpus into a plurality of subsets, executing steps 102,104, 106, 108, 110, 112, 114, and 116 for each subset, and thencombining results from each subset into a single data set prior toexecuting step 118. Thereafter, steps 118, 120, 122, 124, and 126 wouldin at least one embodiment be performed by a single server operating ona data set reflecting the results of all subsets. In this way, in someembodiments, a MapReduce capability would provide computationalefficiencies and reduce the time to generate a Partition-by-Query index.

Retrieving Search Results from a Partition-By-Query Index

FIG. 5 illustrates a method for retrieving search results from aPartition-by-Query index consistent with at least one embodiment of thepresent invention. In step 502, a server containing a partial or fullPartition-by-Query index receives a search request from a user. In someembodiments, as discussed above in conjunction with FIG. 4, the user'ssearch request may be forwarded to the server from a proxy. In otherembodiments not employing a proxy, a server may receive the searchrequest from a user without employing a proxy.

In step 504, a concept-ordered list of search tokens is created from thesearch request received from the user in step 502. As previouslydiscussed, concept-ordered lists of tokens reorder tokens to place termswith low frequency in the corpus first and to place tokens of decreasingfrequency in subsequent positions within the concept-ordered list.Additionally, as discussed above, some tokens such as articles may beremoved from the search request in some embodiments.

In step 506, a scan( ) function is performed utilizing theconcept-ordered list of tokens as an input to the scan( ) function. Aspreviously discussed, the scan( ) function retrieves the firstkey-values pair following the key in the index where the concept-orderedlist of tokens would exist. The values for the key-values pair retrievedby the scan( ) function is a cluster in some embodiments of the presentinvention. In other embodiments not employing clustering, the valuesretrieved may represent an ordered list of documents that may bepresented as a search result to the user.

In step 508, for embodiments employing clustering, the key-values paircorresponding to the concept-ordered list of tokens within the clusterretrieved in step 606 is retrieved from the cluster to form the list ofdocuments responsive to the search request.

In step 510, method 500 concludes by communicating a message to the usercomprising an ordered list of documents responsive to the user's searchrequest.

Suggestions and Spelling Corrections Via Confusion Sets

The Partition-by-Query approach can be extended to provide suggestionsand spelling corrections to users. A suggestion is a series of suggestedsearch terms that are proposed to a user as the user types charactersinto a search engine user interface. Suggestions extend the characters ausers has entered into the search engine user interface to proposetokens that are known to exist in the corpus. For example, if a usertyped the letters “presid” into a search engine user interface, thesearch engine could provide suggestions that would complete the user'styping to reflect tokens in the corpus, such as “president”, “preside”,“presidential”, and “presidio.”

A spelling correction is a series of suggested search terms that areproposed to a user reflecting tokens that are present in the corpus,each of which may reflect changes to the characters a user has typedinto a search engine user interface. Spelling corrections may take twoforms: changing a token that does not represent a word in the corpus toreflect a token that is present in the corpus (e.g., changing “hllo”into “hello”) and changing a token present into the corpus into ahomonym of that token, also present in the corpus (e.g., changing “main”into “Maine”).

Embodiments present several suggestions or spelling corrections for eachtoken entered by a user. For example, if a user typed the letters “main”into a search engine user interface, the search engine could providespelling corrections reflecting tokens in the corpus such as “Maine”,“man”, “mainly”, “pain”, etc.

Users expect that modern search engines will make useful suggestions andcorrect spelling mistakes in real time; i.e., as a user types charactersinto the search engine user interface. Conventional search engines havea difficult time meeting users' expectations that useful suggestions andspelling corrections be provided in real time because conventionalsearch engines compute variations of tokens that the user enters in realtime and perform lookups for each term variation to determine whetherthe term variation is a token in the corpus. Therefore, some searchengines provide suggestions or spelling corrections involving tokensthat are less useful than other tokens due to the limited time thesearch engine has to generate such suggestions and spelling corrections.As a result, users may not be satisfied with the suggestions andspelling corrections produced by conventional search engines.

The Partition-by-Query approach can be extended to generate the “mostappropriate” suggestions and spelling corrections for a given token inreal time, thereby satisfying users' expectations for useful real timesuggestions and spelling corrections. The collection of suggestions andspelling corrections for a particular token is referred to hereinafteras a “confusion set” for that token.

A confusion set contains the most appropriate suggestions and spellingcorrections if it includes the suggestions and spelling correctionslikely to reflect what the user intended when typing a particular tokeninto a search engine user interface. Generally, suggestions and spellingcorrections reflecting small changes to users' tokens (e.g., changing oradding one character) better reflect what users intended thansuggestions and spelling corrections reflecting large changes to users'tokens (e.g., changing or adding characters reflecting 50% or more ofwhat the user has entered into the search engine user interface).Therefore, the most appropriate suggestions and spelling corrections arethose reflecting small changes to tokens entered by users. Accordingly,the confusion set generated by the Partition-by-Query approachprioritizes suggestions and spelling corrections reflecting smallchanges to users' tokens over large changes to users' tokens.

Providing suggestions and spelling corrections is a two-step process.First, a collection of suggestions and spelling corrections relatingtokens a user could enter to tokens in the corpus is generated prior tothe search engine receiving search requests against thePartition-by-Query index. Second, as a user types tokens into a searchengine user interface, variations of those tokens are computed in realtime and used to identify suggestions and spelling corrections that arepresented to the user. This combination of generating suggestions andspelling corrections for each token in the corpus prior to receivingsearch queries and providing those suggestions and spelling correctionsto users in real time as they type tokens into the search engine userinterface can provide a solution for suggestions and spellingcorrections that meets or exceeds users' expectations for qualityresponsiveness of the search engine. A discussion of methods and systemsimplementing these concepts follows.

FIG. 6 illustrates a method 600 for generating a confusion setconsistent with at least one embodiment. In step 602, residual stringswith corresponding weights for each token in the corpus are generated. A“residual string” is a one-character or multi-character variation from atoken. Variations from a token can represent character additions,character modifications, or character removals. For example, for token“bell,” “belly,” “tell,” and “bel” represent residual strings that add,modify, or remove characters. Residual strings do not necessarilyrepresent proper words or tokens in the corpus. As noted above, “bel” isa residual string for token “bell” although “bel” is not a properEnglish word and it may not represent a token in a corpus. Misspellings,by their nature, may represent expressions that are not proper words ortokens in the corpus. Residual strings, however, can relate misspellingsto tokens that are in the corpus.

Residual strings for a token have associated weights representing thenumber of character variations between the token and the residualstring. For example, token “bell” varies by one character from residualstring “belly.” Therefore, the weight for residual string “belly”relative to token “bell” is one. Residual strings can have weightsgreater than one. For example, “bellow” is a residual string for token“bell” with a weight of two.

Residual strings can have different weights associated with differenttokens. For example, residual string “bellow” may have a weight of tworelative to token “bell” and a weight of one relative to token “below.”

Residual strings for a token can be created by adding, modifying, orremoving characters in each position of the token. For token “bell,”residual strings can be created by adding characters between each letterof token “bell” (e.g., “baell,” “bbell,” etc) modifying each letter oftoken “bell” (e.g., “cell,” “bfll”, etc) and removing each letter oftoken “bell” (e.g., “ell,” “bll,” and “bel”). Step 102 identifies aseries of such residual strings and corresponding weights for each tokenin the corpus.

FIG. 7A illustrates a series of exemplary residual strings 700 withcorresponding weights for token “GEORGIA” 702. As shown, token “GEORGIA”702 has residual string “GEORGA” 704 of weight 1, residual string“GEORA” 706 of weight 2, residual string “GERGA” 708 of weight 2,residual string “GERA” 710 of weight 2, residual string “GEOR” 712 ofweight 3, and residual string “GEORG” 714 of weight 2. Some possibleresidual strings for token “GEORGIA” were omitted from FIG. 7A forclarity purposes.

After residual strings and corresponding weights have been generated foreach token in the corpus in step 602, step 604 creates associationsbetween each token and residual strings associated with other tokens.For example, as illustrated in FIG. 7B, in at least one embodiment,residual strings “GEORG” 714 and “GEOR” 712 have weights of one and two,respectively, relative to token “GEORGE” 716. Prior to step 604, aresidual string “GEORG” 714 may have separately existed for token“GEORGE” 716 and token “GEORGIA” 702. Upon completion of step 604,however, only a single instance of each individual string will exist.

In step 606, a “producer list” is created for each token. The producerlist for a token comprises its residual strings and correspondingweights. For example, a producer list for token “GEORGE” 716 may be“{GEORG, 1}; {GEOR, 2}.”

In step 608, the producer list for each token is propagated to eachresidual string to form a “variation list” for that residual string. Avariation list for a residual string represents the tokens that can beformed from the residual string and the number of character variationsbetween the residual string and the token. For example, the variationlist for residual string “GEORG” 714 is “{GEORGE, 1}; {GEORGIA, 2}.” Asdiscussed in conjunction with FIG. 8, the tokens in a variation list fora residual string provide suggestions and spelling corrections for thatresidual string and the number of character variations for each token inthe variation list allows the suggestions and spelling corrections to beprioritized.

In some embodiments, only tokens with less than a certain number ofcharacter variations are propagated to residual strings to formvariation lists for those residual strings. For example, in at least oneembodiment, only tokens with less than 5 character variations to aresidual string are propagated to that residual string. In otherembodiments, the certain number of allowable character variations may begreater than 5 or less than 5.

In step 610, tokens in the corpus and their corresponding weights arepropagated to other tokens. For example, the propagated list of tokensfor token “GEORGE” 716 may be “{GEORGIA, 3},” reflecting three charactervariations from token “GEORGE” 716 through residual string “GEORG” 714to token “GEORGIA” 702. In some embodiments, only tokens withcorresponding weights below a certain limit are propagated to othertokens. For example, in at least one embodiment, only tokens withcorresponding weights less than 5 are propagated to other tokens. Inother embodiments, the limit may be greater than 5 or less than 5.

In step 612, some redundant residual strings may be discarded. Forexample, as illustrated in FIG. 7B, residual string “GEORG” 714 isredundant to residual string “GEOR” 712 because each of the associationsbetween residual string “GEORG” 714 and other tokens or residual stringsare represented in other residual strings. Therefore, residual string“GEORG” 714 may be removed to reduce the size of the residual stringdata set. However, tokens and high connectivity residual nodes areretained rather than removed during step 612.

In step 614, the propagated list of tokens with weights for each tokenand the variation list for each remaining residual string are stored asthe confusion set for the corpus. In step 616, the method may concludeby creating a Bloom filter for the confusion set. Those skilled in theart will recognize how to create a Bloom filter. As discussed inconjunction with FIG. 8, a Bloom filter may be used to determine whethersuggestions or spelling corrections exist for a current set ofcharacters entered by a user into a search engine user interface.

FIG. 8 illustrates an exemplary method 800 for providing suggestions andspelling corrections to users based in part on a confusion set. In step802, a search engine utilizing a confusion set receives one or morecharacters as input to the search engine from a user. In someembodiments, each character entered by a user may be individuallycommunicated to the search engine. In other embodiments, upon acharacter being entered by the user, the search engine may receive allcharacters that the user has entered for the current token or partialtoken as input to the search engine.

In step 804, the characters entered by the user for the current token orpartial token are input to a Bloom filter and the Bloom filterdetermines whether those characters represent suggestions or spellingcorrections for those characters in the confusion set. In step 806, datarepresenting suggestions or spelling corrections for the charactersreceived from the user is retrieved from the confusion set if the Bloomfilter determined that such data was present in the confusion set. Instep 808, method 800 concludes by presenting the spelling corrections orsuggestions in the data retrieved from the confusion set to the user assearch options.

One benefit of the method discussed above is that suggestions andspelling corrections can be provided to a user based on one or possiblyonly a few references to stored data. By contrast, existing methodstypically employ numerous reads from stored data and therefore lead topoor responsiveness to user input and resulting failure to meet users'expectations.

Use of the Principles of Triangle Inequality to Limit Suggestions

Contemporary search engines suffer from an inability to provideeffective, ranked suggestions to a user in real time, particularly inthe presence of one or more user misspellings. Part of this problemarises from the real-time nature of computing suggestions and providingthem to the user as the user enters characters into a search engine userinterface. Another part of this problem arises from contemporary searchengines' inability to recognize which suggestions are inherently notuseful and thus not worth computing or providing to a user.

A users' perception of a search engine is based in part on the relevanceof suggestions provided to the user and the responsiveness of the searchengine's interface as it provides suggestions in response to users'keystrokes. Contemporary search engines either provide relevantsuggestions but with a slow response time, which may be unacceptable tousers, or provide timely suggestions that are not as relevant as theycould be if more suggestion analysis could be performed, which may alsobe unacceptable to users. As a result, users may be dissatisfied withcontemporary search engines.

Triangle Inequality principles may be used to provide very relevantsuggestions to search engine users in real time. Using the principles ofTriangle Inequality, relationships between a query, suggestions for thatquery, and documents in the corpus may be established. The principles ofTriangle Inequality provide a criterion for identifying whichsuggestions among the most relevant suggestions are worth analyzing andpresenting to a user, thereby allowing other suggestions to be omittedfrom analysis. Reducing the number of suggestions that are analyzed inreal time, without sacrificing the relevance of suggestions subsequentlypresented to a user, allows a search engine employing the principles ofTriangle Inequality to meet users' expectations for suggestion relevanceand responsiveness.

Triangle Inequality concerns relationships between a query, a document,and a suggestion relating the query and the document. As discussed inconjunction with FIGS. 6, 7A, 7B, and 8, suggestions may be formedthrough use of a confusion set, reflecting a series of suggestions for aparticular token provided to a search engine by a user. A “confusionmatrix” is the product of confusion sets for the tokens a user has inputto a search engine; i.e., a confusion matrix comprises all combinationsof the elements of each confusion set. The Triangle Equality allows someor many elements of the confusion matrix to be removed fromconsideration when presenting suggestions to a user.

A confusion matrix may be formed as follows. In one example, a userinputs the string “Persident Obam” into a search engine and the searchengine parses that string into two tokens: “Persident” and “Obam.” Inthis example, a confusion set for “Persident” may be {“president” and“preside”} and a confusion set for “Obam” may be {“Obama” and“Alabama”}. A confusion matrix for these confusion sets would be theproduct of the terms: {“president Obama”; “president Alabama”; “presideObama”; and “preside Alabama”}.

Bloom filtering the confusion matrix may allow some irrelevantsuggestions to be removed. Those skilled in the art recognize that aBloom filter may quickly identify strings that are not present in adataset. Therefore, a Bloom filter can eliminate elements of a confusionmatrix by identifying elements whose associated token suggestions arenot present in a document within the corpus. Such suggestions can beremoved from the confusion matrix because they do not representsuggestions that would be relevant to the user's query; i.e., suchsuggestions are not conjunctive keys for the corpus.

The principles of Triangle Inequality are expressed in thisrelationship:θ_(d,q)≥θ_(q,s)+θ_(s,d)

Θ_(q,s) represents a vector-space angle between a query input by a userand a suggestion for that query. This angle reflects a differencebetween the query and the suggestion due to misspellings in the query,because suggestions are comprised of tokens in a document as discussedin conjunction with FIGS. 6-8.

Θ_(s,d) represents the vector-space angle between a suggestion and adocument. Θ_(s,d) is greater than or equal to Θ_(q,s) because thedocument may include tokens not present in the user's query. If thedocument only comprises tokens in the user's query, Θ_(s,d) will beequal to Θ_(q,s); otherwise Θ_(s,d) will be greater than Θ_(q,s).

Θ_(d,q) is referred to hereinafter as a “stopping criteria” andrepresents a vector-space angle between a query input by a user and adocument. The value for Θ_(d,q) reflects that Θ_(q,s) (the anglereflecting spelling errors in the query) is propagated into the documentwhere additional terms, not present in the query, reflected by Θ_(s,d),increase the angle further.

Thus, the principles of Triangle Inequality reflect that thevector-space angle between a document and a user's query must be greaterthan or equal to the sum of a vector space angle between the query and asuggestion for that query and a vector space angle between thesuggestion and the document.

Application of the principles of Triangle Inequality to suggestionranking allows a determination that a set of suggestions in theconfusion matrix are more relevant than all other suggestions in theconfusion matrix based in part on the stopping criteria. Therefore, whena search engine evaluates which suggestions to provide to a user, thesearch engine may omit the other suggestions in the confusion matrixfrom evaluation. Reducing the number of suggestions that are analyzed inreal time may tend to improve the responsiveness of the search engine touser input while nonetheless presenting the most relevant suggestions tothe user.

Vector-space angles may relate a user's query to a string in a documentwithin a corpus through a mapping function described as follows. Forquery Q and document string D, the similarity between Q and D may beexpressed as the cosine of the vector-space angle between Q and D. Thoseskilled in the art will recognize that the equation that follows allowsthis vector-space angle to be computed for a two-token query involvingone misspelled token:

${\cos\left( \theta_{d,q} \right)} = {{s \cdot \mu^{2}} + \frac{\lambda^{2}}{\left( {{D} \cdot \sqrt{\mu^{2} + \lambda^{2}}} \right)}}$

In at least one embodiment, for the equation above, λ represents theTF-IDF value for a first token of the two-token query, and μ representsthe TF-IDF value for a second token of the two-token query.Additionally, s represents the similarity between one of the two tokensand a suggestion for that token, and “|D|” represents the magnitude ofthe vector formed by document D. Those skilled in the art will recognizethat the equation above can be extended to address queries with morethan two tokens and more than one misspelling.

FIG. 9 illustrates a method 900 applying the principles of TriangleInequality to identify one or more suggestions for a search query. Instep 902, a confusion set is generated for each token in a search query.In step 904, a confusion matrix is generated from the confusion setsgenerated in step 902. In step 906, elements of the confusion matrixwhose suggestions are not present in a document within a corpus areremoved from the confusion matrix. In step 908, the remainingsuggestions are ranked by their corresponding Θ_(q,s) value. In step910, a first ranked suggestion is selected and its associated documentis also selected. In step 912, Θ_(q,s) is computed for the currentsuggestion.

In step 914, a determination is made whether Θ_(q,s) for the currentselected suggestion is greater than or equal to Θ_(d,q) for a documentassociated with the previous selected suggestion. If step 914 evaluatesto false, the stopping criteria discussed above has not been met, soΘ_(d,q) is determined for the document associated with the currentselected suggestion in step 916, a next selected suggestion isidentified in step 918, and the method returns to step 912. If step 914evaluates to true, however, the stopping criteria discussed above hasbeen met so the method concludes by presenting the selected suggestionsto a user in step 920.

As discussed above, method 900 utilizes the principles of TriangleInequality to select suggestions that are the most relevant to a user,based on a computed stopping criteria. This method may reduce the numberof suggestions that a search engine considers and therefore improve theresponsiveness of the search engine as perceived by a user.

Generating Confusion Matrices for Multi-Word Tokens

Multi-word tokens, such as “San Francisco,” may present challenges to asearch engine because the relationship between the tokens in amulti-word token lead to TF-IDF scores for the multi-word token thatdiffer from scores for the individual words. Stated differently,documents discuss “San Francisco” at a different rate than the samedocuments discuss “San” and “Francisco.” Therefore, providing reliablesuggestions to a user may involve treating multi-word tokens differentlythan single-word tokens.

An additional benefit to treating multi-word tokens differently thansingle-word tokens is that treatment of multi-word tokens tends toreduce the length of search strings, which reduces processing time andstorage costs for a Partition-by-Query index. For example, “SanFrancisco vacation” could be parsed as three single-word tokens or onemulti-word token and one single-word token (i.e., two tokens total). Asnoted above, having fewer tokens reduces processing time and storagecosts so multi-word tokens are desirable.

Identification of multi-word tokens may involve considering all possiblecombinations of tokens to determine which tokens comprise multi-wordtokens. More specifically, for T tokens provided by a user, T−1 spacesexist between tokens and 2^(T-1) combinations of the T tokens could formmulti-word tokens.

Unfortunately, each combination in the 2^(T-1) combinations may involvea confusion set whose term probabilities are computed. Fortunately, T istypically relatively small (e.g., T=5) and reduced forms of confusionmatrices can be formed from a subset of values within the confusionsets.

Specifically, reduced forms of confusion matrices can be formed by onlyconsidering the first row of a confusion matrix, which is formed fromthe first elements of each corresponding confusion set. Other rows inthe confusion matrix, formed from elements other than the first elementin each corresponding confusion set, may have less relevant results andwould therefore be less useful for analysis of multi-word tokens. Fromthe rows of the reduced forms of confusion matrices, each row is rankedby similarity to the combination of the individually spell-corrected(via confusion sets) user tokens, and the B most similar rows areretained for handling multi-word tokens. In some embodiments, B=10,resulting in only ten or fewer rows to be considered during multi-wordtoken analysis.

In addition to the methods disclosed herein, systems may execute themethod or may contain instructions that, when executed, perform thesteps of the method. For example, a first computing device may comprisea processor and a memory, the memory storing instructions that, whenexecuted, perform one or more of the above-disclosed methods.Additionally, a first computer-readable medium may comprise instructionsthat, when executed, perform one or more of the disclosed methods.

The foregoing discussion sets forth methods and systems for providingsearch query results utilizing a Partition-by-Query index. Although themethod and system has been described in the context of a series ofembodiments, those skilled in the art will readily recognize that themethods and systems suggest other embodiments without departing from thescope of the method and system.

The invention claimed is:
 1. A method for providing suggested searchqueries in response to a search query, the method comprising:generating, by a computing device, a confusion set for each token in thesearch query, wherein each confusion set comprises a plurality ofresidual strings formed from a token of the search query by adding,modifying, or removing characters in each position of the token, andother tokens associated with the residual strings, wherein for at leastone token in the search query there is at least one residual string inthe confusion set that varies from the token by having at least onecharacter that is not present in the token; generating, by the computingdevice, a confusion matrix from the confusion sets, wherein theconfusion matrix comprises entries that are the products of the residualstrings and tokens in the confusion sets, wherein the entries formpotential suggested search queries; ranking, by the computing device,the suggested search queries in the confusion matrix by a vector spaceangle between the search query and the suggested search queries, whereinthe search query and each suggested search query is associated with avector; selecting, by the computing device, each ranked suggested searchquery having a vector space angle between the search query and theranked suggested search query that is less than a vector space anglebetween the search query and a document associated with a higher-rankedsuggested search query; and presenting the selected suggested searchqueries on a computing device.
 2. The method of claim 1, furthercomprising the step of generating, by the computing device, a Bloomfilter for the confusion matrix, wherein each element of the Bloomfilter corresponds to an entry in the confusion matrix, and removingentries from the confusion matrix using the Bloom filter.
 3. The methodof claim 1, wherein the search query comprises a plurality of tokensincluding a first token and a second token, and the vector space anglebetween the search query and a particular document is determined basedon: a TF-IDF value of the first token, a TF-IDF value of the secondtoken, a similarity between a particular token from the plurality oftokens and a suggestion for the particular token, and a magnitude of avector formed by the document.
 4. The method of claim 1, wherein theconfusion matrix is a reduced form confusion matrix formed from aselected element of a confusion set while excluding the remainingelements of the confusion set.
 5. The method of claim 4, furthercomprising: identifying a subset of rows of the reduced form confusionmatrix by ranking the rows based on a similarity of each row of theconfusion matrix to a combination of individually spell-correctedtokens; and combining a plurality of single-word tokens to obtain amulti-word token.
 6. An apparatus for generating a list of the mostrelevant suggestions or spelling corrections to a search engine userfrom a collection of suggestions or spelling corrections, the apparatuscomprising: means for generating, by a computing device, confusion setsfor each token in a search query, wherein each confusion set comprises aplurality of residual strings formed from a token of the search query byadding, modifying, or removing characters in each position of the token,and other tokens associated with the residual strings; means forgenerating, by the computing device, a confusion matrix from theconfusion sets, wherein the confusion matrix comprises entries that arethe products of the residual strings and tokens in the confusion sets,wherein the entries form potential suggested search queries; means forranking, by the computing device, the suggested search queries in theconfusion matrix by a vector space angle between the search query andthe suggested search queries, wherein the search query and eachsuggested search query is associated with a vector; and means forselecting, by the computing device, each ranked suggested search queryhaving a vector space angle between the search query and the rankedsuggested search query that is less than a vector space angle betweenthe search query and a document associated with a higher-rankedsuggested search query.
 7. The apparatus of claim 6, further comprisingthe step of generating, by the computing device, a Bloom filter for theconfusion matrix, wherein each element of the Bloom filter correspondsto an entry in the confusion matrix, and removing entries from theconfusion matrix using the Bloom filter.
 8. The apparatus of claim 6,wherein the search query comprises a plurality of tokens including afirst token and a second token, and the vector space angle between thesearch query and a particular document is determined based on: a TF-IDFvalue of the first token, a TF-IDF value of the second token, asimilarity between a particular token from the plurality of tokens and asuggestion for the particular token, and a magnitude of a vector formedby the document.
 9. The apparatus of claim 6, wherein the confusionmatrix is a reduced form confusion matrix formed from a selected elementof a confusion set while excluding the remaining elements of theconfusion set.
 10. The apparatus of claim 9, further comprising: meansfor identifying a subset of rows of the reduced form confusion matrix byranking the rows based on a similarity of each row of the confusionmatrix to a combination of individually spell-corrected tokens; andmeans for combining a plurality of single-word tokens to obtain amulti-word token.
 11. A computer-readable memory, storing instructionsfor providing suggested search queries in response to a search query,the instructions performing steps comprising: generating, by a computingdevice, a confusion set for each token in the search query, wherein eachconfusion set comprises a plurality of residual strings formed from atoken of the search query by adding, modifying, or removing charactersin each position of the token, and other tokens associated with theresidual strings, wherein for at least one token in the search querythere is at least one residual string in the confusion set that variesfrom the token by having at least one character that is not present inthe token; generating, by the computing device, a confusion matrix fromthe confusion sets, wherein the confusion matrix comprises entries thatare the products of the residual strings and tokens in the confusionsets, wherein the entries form potential suggested search queries;ranking, by the computing device, the suggested search queries in theconfusion matrix by a vector space angle between the search query andthe suggested search queries, wherein the search query and eachsuggested search query is associated with a vector; selecting, by thecomputing device, each ranked suggested search query having a vectorspace angle between the search query and the ranked suggested searchquery that is less than a vector space angle between the search queryand a document associated with a higher-ranked suggested search query;and presenting the selected suggested search queries on a computingdevice.
 12. The computer-readable memory of claim 11, wherein theinstructions further perform the step of generating, by the computingdevice, a Bloom filter for the confusion matrix, wherein each element ofthe Bloom filter corresponds to an entry in the confusion matrix, andremoving entries from the confusion matrix using the Bloom filter. 13.The computer-readable memory of claim 11, wherein the search querycomprises a plurality of tokens including a first token and a secondtoken, and the vector space angle between the search query and aparticular document is determined based on: a TF-IDF value of the firsttoken, a TF-IDF value of the second token, a similarity between aparticular token from the plurality of tokens and a suggestion for theparticular token, and a magnitude of a vector formed by the document.14. The computer-readable memory of claim 11, wherein the confusionmatrix is a reduced form confusion matrix formed from a selected elementof a confusion set while excluding the remaining elements of theconfusion set.
 15. The computer-readable memory of claim 14, wherein theinstructions are for further performing the steps of: identifying asubset of rows of the reduced form confusion matrix by ranking the rowsbased on a similarity of each row of the confusion matrix to acombination of individually spell-corrected tokens; and combining aplurality of single-word tokens to obtain a multi-word token.
 16. Acomputer system comprising: one or more processors; and acomputer-readable memory, storing instructions for providing suggestedsearch queries in response to a search query, the instructionsperforming steps comprising: generating, by a computing device, aconfusion set for each token in the search query, wherein each confusionset comprises a plurality of residual strings formed from a token of thesearch query by adding, modifying, or removing characters in eachposition of the token, and other tokens associated with the residualstrings, wherein for at least one token in the search query there is atleast one residual string in the confusion set that varies from thetoken by having at least one character that is not present in the token;generating, by the computing device, a confusion matrix from theconfusion sets, wherein the confusion matrix comprises entries that arethe products of the residual strings and tokens in the confusion sets,wherein the entries form potential suggested search queries; ranking, bythe computing device, the suggested search queries in the confusionmatrix by a vector space angle between the search query and thesuggested search queries, wherein the search query and each suggestedsearch query is associated with a vector; selecting, by the computingdevice, each ranked suggested search query having a vector space anglebetween the search query and the ranked suggested search query that isless than a vector space angle between the search query and a documentassociated with a higher-ranked suggested search query; and presentingthe selected suggested search queries on a computing device.
 17. Thecomputer system of claim 16, wherein the computer-readable memory storesinstructions for further performing the step of generating, by thecomputing device, a Bloom filter for the confusion matrix, wherein eachelement of the Bloom filter corresponds to an entry in the confusionmatrix, and removing entries from the confusion matrix using the Bloomfilter.
 18. The computer system of claim 16, wherein the search querycomprises a plurality of tokens including a first token and a secondtoken, and the vector space angle between the search query and aparticular document is determined based on: a TF-IDF value of the firsttoken, a TF-IDF value of the second token, a similarity between aparticular token from the plurality of tokens and a suggestion for theparticular token, and a magnitude of a vector formed by the document.19. The computer system of claim 16, wherein the confusion matrix is areduced form confusion matrix formed from a selected element of aconfusion set while excluding the remaining elements of the confusionset.
 20. The computer system of claim 19, wherein the computer-readablememory stores further instructions for performing the steps of:identifying a subset of rows of the reduced form confusion matrix byranking the rows based on a similarity of each row of the confusionmatrix to a combination of individually spell-corrected tokens; andcombining a plurality of single-word tokens to obtain a multi-wordtoken.